Artificial Intelligence for Earthquake Prediction
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Earth Sciences".
Deadline for manuscript submissions: 20 November 2025 | Viewed by 2562
Special Issue Editor
Interests: artificial intelligence applications in GNSS; deformation monitoring; GNSS algorithms; robust estimation and optimization problems in cartographic sciences
Special Issue Information
Dear Colleagues,
The flourishing of artificial intelligence in its various forms, from deep learning to generative artificial intelligence, has made it possible in recent years to perform tasks that previously seemed out of reach or completely impossible. Its immense potential for accelerating scientific discovery cannot be ignored in any branch of scientific research today.
One of the topics considered as an impossible task for centuries is the prediction of a future earthquake based on some possible earthquake precursors. Strange animal behaviors prior to large earthquakes have, however, been documented for many decades, and it seems plausible that these anomalous behaviors could be triggered by certain changes in the environment that are perceptible in the earth's surface, the water, and the atmosphere. Inspired by this idea, some researchers have attained a relative degree of success in analyzing certain earthquake precursors. Many successful detection cases have been published in recent years; however, a method producing consistent results in the long run is still missing.
With these partial successes and the accelerated development of artificial intelligence, consistent earthquake prediction seems to have increasingly become a matter of near development rather than impossibility. We may not yet understand the underlying processes exactly, but it may be that the predictive essences can still be captured. Science is starting to accept that, perhaps, earthquake prediction is only as impossible as weather forecasting was considered some centuries ago.
In the current Special Issue, we welcome contributions from all AI disciplines focusing on earthquake prediction from data sources including, but not limited to, anomalies in the Total Electron Content (TEC) of the ionosphere or other ground- or space-based recorded anomalies of electromagnetic nature, b-values and other estimators from the seismic data catalog, crustal strain variations, geomagnetic data, and geoacoustics signals.
We especially encourage contributions that aim to produce a robust forecasting system with broad geographic application over time.
Dr. Sergio Baselga
Guest Editor
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Keywords
- earthquake forecast
- artificial intelligence
- machine learning
- deep learning
- artificial neural networks (ANN)
- random forests
- support vector machines (SVM)
- ionospheric anomalies
- total electron content (TEC)
- historical seismicity
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